Online social networking sites and tools such as Facebook™, Pinterest™ Tumblr™, Google+™ Hootsuite™, and Twitter™ have changed the way people share information and otherwise communicate with each other. Even in business environments, collaborative sites enable groups of related users share information about sales opportunities or other issues surrounding products or services pursued or offered by the team. Presently known enterprise social network platforms such as Chatter™ by Salesforce™ provide users with a feed-based stream of posts for tracked objects.
In addition to the basic utility of these social media sites, the generated content may provide further insights about the people and discussion topics. One mechanism by which social media may be used by an organization is sentiment analytics (or analysis) that attempts to evaluate the reactions or feelings about postings on a particular topic, e.g., to monitor user preference on products in a marking campaign. However, social media platforms collectively generate a large amount of data that may be challenging to collect and analyze in a meaningful manner. Moreover, even if a suitable sentiment analysis may be modeled for an individual user, such models may not be applicable to other users. On the other hand, generic models may be inaccurate or otherwise unsuited for individual clients.
Accordingly, it is desirable to provide improved systems and methods for collecting, administering, and evaluating sentiment in social media data, particularly for individual clients or users. Furthermore, other desirable features and characteristics will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.